Bob Rehder and Michael Waldmann

Failures of explaining away and screening off in described versus experienced causal learning scenarios

Memory & Cognition

Causal Bayes nets capture many aspects of causal thinking that set them apart from purely associative reasoning. However, some central properties of this normative theory routinely violated. In tasks requiring an understanding of explaining away and screening off, subjects often deviate from these principles and manifest the operation of an associative bias that we refer to as the richget- richer principle. This research focuses on these two failures comparing tasks in which causal scenarios are merely described (via verbal statements of the causal relations) versus experienced (via samples of data that manifest the intervariable correlations implied by the causal relations). Our key finding is that we obtained stronger deviations from normative predictions in the described conditions that highlight the instructed causal model compared to those that presented data. This counterintuitive finding indicate that a theory of causal reasoning and learning needs to integrate normative principles with biases people hold about causal relations. (PsycINFO Database Record (c) 2017 APA, all rights reserved)

Accession Number: 2016-54623-001. PMID: 27826953 Partial author list: First Author & Affiliation: Rehder, Bob; Department of Psychology, New York University, New York, NY, US. Other Publishers: Psychonomic Society. Release Date: 20161114. Correction Date: 20170316. Publication Type: Journal (0100), Peer Reviewed Journal (0110). Format Covered: Electronic. Document Type: Journal Article. Language: English. Major Descriptor: Errors; Learning; Reasoning; Thinking. Minor Descriptor: Causality. Classification: Learning & Memory (2343). Population: Human (10). Methodology: Empirical Study; Mathematical Model; Quantitative Study. Page Count: 16. Issue Publication Date: Feb, 2017. Publication History: First Posted Date: Nov 8, 2016. Copyright Statement: Psychonomic Society, Inc. 2016.